Control Of Quadrotor Unmanned Aerial Vehicle Research Articles

Adaptive Terminal Sliding Mode Control for Attitude and Position Tracking Control of Quadrotor UAVs in the Existence of External Disturbance

In this paper, a 6-Degrees-of-Freedom (6-DOF) Unmanned Aerial Vehicle (UAV) system with external disturbance corresponding to sensor failure is considered. The control method is presented in two parts. In the first part, the upper bound of external disturbance is known and a Proportional-Integral-Derivative (PID) Sliding Mode Control (SMC) technique is planned for maintaining the desired position in the finite time. Whereas, the upper bound of the external disturbance is considered unknown in the second part and the adaptive PID-SMC method is offered for stability and position tracking control of UAV systems. Using the Lyapunov stability notion, the offered control method proves that the quadrotor's states can be tracked and stabilized in the finite time. Moreover, for the approximation of unknown bound of the external disturbances which are entered in the quadrotor dynamic model at any moment, adaptive control laws have been applied. Finally, simulation outcomes are provided to display the efficiency of the recommended technique.

Optimal backstepping control of quadrotor UAV using gravitational search optimization algorithm

Quadrotor unmanned aerial vehicle (UAV) has superior characteristics such as ability to take off and land vertically, to hover in a stable air condition and to perform fast maneuvers. However, developing a high-performance quadrotor UAV controller is a difficult problem as quadrotor is an unstable and underactuated nonlinear system. The effort in this article focuses on designing and optimizing an autonomous quadrotor UAV controller. First, the aerial vehicle's dynamic model is presented. Then it is suggested an optimal backstepping controller (OBC). Traditionally, backstepping controller (BC) parameters are often selected arbitrarily. The gravitational search algorithm (GSA) is used here to determine the BC parameter optimum values. In the algorithm, the control parameters are calculated using an integral absolute error to minimize the fitness function. As the control law is based on the theorem of Lyapunov, the asymptotic stability of the scheme can be ensured. Finally, several simulation studies are conducted to show the efficacy of the suggested OBC.

Information fusion estimation-based path following control of quadrotor UAVs subjected to Gaussian random disturbance

Random disturbance has a detrimental effect on the reliability and safety of quadrotor unmanned aerial vehicles (UAVs). This paper proposes an anti-Gaussian random disturbance control method for the path following of a quadrotor UAV. The quadrotor system is linearized and divided into two subsystems, i.e., a translational subsystem and a rotational subsystem, and hierarchical strategy is used to design the overall control architecture. In order to suppress the negative effects of Gaussian random disturbances and simplify the design process of linear–quadratic optimal output tracking control problem, a new information fusion estimation based robust control named Gaussian information fusion control (GIFC) scheme is proposed. The convergence of the output tracking errors of GIFC system is proved via Lyapunov theory. The proposed GIFC control scheme is employed for position and attitude controller designs to enhance the robustness of the quadrotor system to Gaussian random perturbations. Finally numerical simulation experiments illustrate the effectiveness and robustness of the proposed control strategy.

Toward Visibility Guaranteed Visual Servoing Control of Quadrotor UAVs

This paper studies the visibility problem in the visual servoing control of quadrotor unmanned aerial vehicles (UAVs). Visual servoing is a useful technique for the inspection, positioning, landing, and tracking of quadrotor UAVs. However, one prerequisite in previous research works is that the visual target stays inside the field of view of the camera for all time. While lose sight of the visual target will result in fatal failure of the task, how to guarantee this prerequisite is often overlooked. In this paper, we aim to achieve reliable visual servoing by deriving a provable solution to guarantee visibility in quadrotor visual serving control. The main idea is to ensure the forward invariance of a set using control barrier functions. The set is constructed as the visible set, which specifies the upper bound of the distance between the center of the image plane and the coordinates of visual features in the image plane. Then, the visibility constraint is developed based on control barrier function. The original control inputs are minimally modified to satisfy the visibility constraint, thus preserving visibility. Finally, some simulations and experimental results are presented to validate this method.

Modified Consensus-based Output Feedback Control of Quadrotor UAV Formations Using Neural Networks

In this paper, a novel nonlinear output feedback neural network (NN)-based consensus controller is developed for a group of quadrotor unmanned aerial vehicles (UAVs). One UAV in the group tracks a desired trajectory while the rest of the group uses consensus-based formation controllers without knowledge of the desired trajectory. Each UAV estimates its own and its neighbor’s velocities through a novel nonlinear NN-based observer by using position and orientation information. Neighboring UAV positions and orientation information is assumed to be available via wireless communication or obtained through local sensors. Since quadrotor UAVs have six degree of freedom with only four control inputs, the UAV’s pitch and roll angles are utilized as virtual control inputs to bring all UAVs to consensus points along x and y directions. The Lyapunov stability theorem is utilized to demonstrate that all the position errors, orientation errors, velocity tracking errors, observer estimation errors, and NN weight estimation errors are semi-globally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances. The effectiveness of our consensus-based output feedback formation control of quadrotor UAVs is demonstrated in simulation validating our theoretical claims.

Mixing Adaptive Fault Tolerant Control of Quadrotor UAV

AbstractIn this paper, a multiple model adaptive fault tolerant control scheme is proposed based on mixing of the control signals generated by a set of linear quadratic state feedback controllers. Each of these controllers are designed considering closed loop system performance for a particular range of fault. Stability analysis of the proposed scheme is provided. The paper further presents specific design and implementation for motion control of quadrotor unmanned aerial vehicles (UAVs). The designed mixing adaptive controller is tested via real‐time experiments on Quanser Qball‐X4 UAVs. The experimental results verify the efficiency of the proposed scheme.

Formation Control of Quadrotor UAVs by Vision-Based Positioning

In this paper, a formation control method of quadrotor Unmanned Aerial Vehicles (UAVs) by vision-based positioning is presented. The relative positions and attitudes of two UAVs with respect to a visual marker attached to the third UAV is estimated by a camera calibration method. Based on the estimated positions and attitudes, two UAVs are controlled to the desired positions to form a given formation with respect to the third UAV. A simplified dynamics model of a quadrotor UAV is utilized to design a controller. The proposed formation control method is validated by an experiment with a motion capture system which provides the ground truth of the position data.

Nonlinear ego-motion estimation from optical flow for online control of a quadrotor UAV

For the control of unmanned aerial vehicles (UAVs) in GPS-denied environments, cameras have been widely exploited as the main sensory modality for addressing the UAV state estimation problem. However, the use of visual information for ego-motion estimation presents several theoretical and practical difficulties, such as data association, occlusions, and lack of direct metric information when exploiting monocular cameras. In this paper, we address these issues by considering a quadrotor UAV equipped with an onboard monocular camera and an inertial measurement unit (IMU). First, we propose a robust ego-motion estimation algorithm for recovering the UAV scaled linear velocity and angular velocity from optical flow by exploiting the so-called continuous homography constraint in the presence of planar scenes. Then, we address the problem of retrieving the (unknown) metric scale by fusing the visual information with measurements from the onboard IMU. To this end, two different estimation strategies are proposed and critically compared: a first exploiting the classical extended Kalman filter (EKF) formulation, and a second one based on a novel nonlinear estimation framework. The main advantage of the latter scheme lies in the possibility of imposing a desired transient response to the estimation error when the camera moves with a constant acceleration norm with respect to the observed plane. We indeed show that, when compared against the EKF on the same trajectory and sensory data, the nonlinear scheme yields considerably superior performance in terms of convergence rate and predictability of the estimation. The paper is then concluded by an extensive experimental validation, including an onboard closed-loop control of a real quadrotor UAV meant to demonstrate the robustness of our approach in real-world conditions.

Synopsis of Soft Computing Techniques used in Quadrotor UAV Modelling and Control

The aim of this article is to give an introduction to quadrotor systems with an overview of soft computing techniques used in quadrotor unmanned aerial vehicle (UAV) control, modelling, object following and collision avoidance. The quadrotor system basics, its structure and dynamic model definitions are recapitulated. Further on synopsis is given of previously proposed methods, results evaluated and conclusions drown by authors of referenced publications. The result of this article is a summary of multiple papers on fuzzy logic techniques used in position and altitude control systems for UAVs. Also an overview of fuzzy system based visual servoing for object tracking and collision avoidance is given together with a briefing of quadrotor UAV control techniques efficiency study. Conclusion is that though soft computing methods are widely used with good results, there is still place for much research to be done on find more efficient soft computing tools for simple modelling, robust dynamic control and fast collision avoidance in quadrotor UAV control.

Constant Altitude Flight Control for Quadrotor UAVs with Dynamic Feedforward Compensation

This study addresses the control problem of an unmanned aerial vehicle (UAV) during the transition period when the flying mode changes from hovering to translational motion in the horizontal plane. First, we introduce a compensation algorithm that improves height stabilization and reduces altitude drop. The main principle is to incorporate pitch and roll measurements into the feedforward term of the altitude controller to provide a larger thrust force. To further improve altitude control, we propose the fuzzy logic controller that improves system behavior. Simulation results presented in the paper highlight the effectiveness of the proposed controllers.